Statistical Challenges of Big Brain Network Data

TL;DR

This paper discusses the unique statistical challenges posed by big brain network data, emphasizing its sparse and hierarchical nature, and reviews limitations of current models while proposing alternative approaches.

Contribution

It identifies the specific topological constraints of brain networks and offers new statistical methods to address these challenges.

Findings

Current models have limitations in handling brain network topology.

Brain networks are characterized by sparsity and hierarchy.

New approaches are proposed to better analyze big brain network data.

Abstract

We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and models we can use effectively. We explore the limitations of the current models used in the field and offer alternative approaches and explain new challenges.